Andreas Herzog

5-HT7R/G12 Signaling Regulates Neuronal Morphology and Function in an Age-Dependent Manner

The common neurotransmitter serotonin controls different aspects of early neuronal differentiation, although the underlying mechanisms are poorly understood. Here we report that activation of the serotonin 5-HT7 receptor promotes synaptogenesis and enhances synaptic activity in hippocampal neurons at early postnatal stages. An analysis of Gα12-deficient mice reveals a critical role of G12-protein for 5-HT7 receptor-mediated effects in neurons. In organotypic preparations from the hippocampus of juvenile mice, stimulation of 5-HT7R/G12 signaling potentiates formation of dendritic spines, increases neuronal excitability, and modulates synaptic plasticity. In contrast, in older neuronal preparations, morphogenetic and synaptogenic effects of 5-HT7/G12 signaling are abolished. Moreover, inhibition of 5-HT7 receptor had no effect on synaptic plasticity in hippocampus of adult animals. Expression analysis reveals that the production of 5-HT7 and Gα12-proteins in the hippocampus undergoes strong regulation with a pronounced transient increase during early postnatal stages. Thus, regulated expression of 5-HT7 receptor and Gα12-protein may represent a molecular mechanism by which serotonin specifically modulates formation of initial neuronal networks during early postnatal development.

Spike-timing-dependent plasticity in small-world networks

Biologically plausible excitatory neural networks develop a persistent synchronized pattern of activity depending on spontaneous activity and synaptic refractoriness (short term depression). By fixed synaptic weights synchronous bursts of oscillatory activity are stable and involve the whole network. In our modeling study we investigate the effect of a dynamic Hebbian-like learning mechanism, spike-timing-dependent plasticity (STDP), on the changes of synaptic weights depending on synchronous activity and network connection strategies (small-world topology). We show that STDP modifies the weights of synaptic connections in such a way that synchronization of neuronal activity is considerably weakened. Networks with a higher proportion of long connections can sustain a higher level of synchronization in spite of STDP influence. The resulting distribution of the synaptic weights in single neurons depends both on the global statistics of firing dynamics and on the number of incoming and outgoing connections.

Restoration of three-dimensional quasi-binary images from confocal microscopy and its application to dendritic trees

For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. For a more detailed analysis, such as the classification of spine types, binary images of higher resolution are required. Simple threshold methods have disadvantages for small structures because the microscope point spread function (PSF) causes a darkening and a spread. The direction-dependent PSF leads to shape errors. To reconstruct structures and edge positions with a resolution smaller than one voxel a parametric model for the dendrite and the spines is created. In our application we use the known tree-like structure of the nerve cell as a- priori information. To create the model, simple geometrical elements (cylinders with hemispheres at the ends) are connected. The model can be adapted for size and position in sub-pixel domain. To estimate the quadratic error between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope PSF. During an iterative process the parameters of the model are optimized. In contrast to other pixel-based methods. the number of variable parameters is much slower. The influence of small deviations in the microscope image (caused by the inhomogeneous biological materials) is reduced.

Acquisition of multiple image stacks with a confocal laser scanning microscope

Image acquisition at high magnification is inevitably correlated with a limited view over the entire tissue section. To overcome this limitation we designed software for multiple image-stack acquisition (3D-MISA) in confocal laser scanning microscopy (CLSM). The system consists of a 4 channel Leica CLSM equipped with a high resolution z- scanning stage mounted on a xy-monitorized stage. The 3D- MISA software is implemented into the microscope scanning software and uses the microscope settings for the movements of the xy-stage. It allows storage and recall of 70 xyz- positions and the automatic 3D-scanning of image arrays between selected xyz-coordinates. The number of images within one array is limited only by the amount of disk space or memory available. Although for most applications the accuracy of the xy-scanning stage is sufficient for a precise alignment of tiled views, the software provides the possibility of an adjustable overlap between two image stacks by shifting the moving steps of the xy-scanning stage. After scanning a tiled image gallery of the extended focus-images of each channel will be displayed on a graphic monitor. In addition, a tiled image gallery of individual focal planes can be created. In summary, the 3D-MISA allows 3D-image acquisition of coherent regions in combination with high resolution of single images.

Displaced strategies optimize connectivity in neocortical networks

This study considers the impact of different connection strategies in developing neocortical networks. An adequate connectivity is a requisite for synaptogenesis and the development of synchronous oscillatory network activity during maturation of cortical networks. In a defined time window early in development neurites have to grow out and connect to other neurons. Based on morphological observations we postulate that the underlying mechanism differs from common strategies of unspecific global or small-world strategies. We show that displaced connection strategies are very effective approaches to connect neurons with minimal wiring costs.

Slow oscillating population activity in developing cortical networks: models and experimental results

During early development neuronal networks express slow oscillating synchronized activity. The activity can be driven by several, not necessarily mutually exclusive, mechanisms. Each mechanism might have distinctive consequences for the phenomenology, formation, or sustainment of the early activity pattern. Here we study the emergence of the oscillatory activity in three computational models and multisite extracellular recordings that we obtained from developing cortical networks in vitro. The modeled networks consist of leaky integrate-and-fire neurons with adaptation coupled via depressing synapses, which were driven by neurons that are intrinsically bursting, intrinsically random spiking, or driven by spontaneous synaptic activity. The activity of model networks driven by intrinsically bursting cells best matched the phenomenology of 1-wk-old cultures, in which early oscillatory activity has just begun. Intrinsically bursting neurons were present in cortical cultures, but we found them only in those cultures that were younger than 3 wk in vitro. On the other hand, synaptically dependent random spiking was highest after 3 wk in vitro. In conclusion, model networks driven by intrinsically bursting cells show a good approximation of the emergent recurrent population activity in young networks, whereas the activity of more mature networks seems to be better explained by spontaneous synaptic activity. Moreover, similar to previous experimental observations, distributed stimulation in the model was more effective in suppressing population bursts than repeated stimulation of the same neurons. This observation can be explained by an effective depression of a larger fraction of synapses by distributed stimulation.

Electron microscopic 3D-reconstruction of dendritic spines in cultured hippocampal neurons undergoing synaptic plasticity

Dendritic spines are assumed to constitute the locus of neuronal plasticity, and considerable effort has been focused on attempts to demonstrate that new memories are associated with the formation of new spines. However, few studies that have documented the appearance of spines after exposure to plasticity‐producing paradigms could demonstrate that a new spine is touched by a bona fida presynaptic terminal. Thus, the functional significance of plastic dendritic spine changes is not clearly understood. We have used quantitative time lapse confocal imaging of cultured hippocampal neurons before and after their exposure to a conditioning medium which activates synaptic NMDA receptors. Following the experiment the cultures were prepared for 3D electron microscopic reconstruction of visually identified dendritic spines. We found that a majority of new, 1‐ to 2‐h‐old spines was touched by presynaptic terminals. Furthermore, when spines disappeared, the parent dendrites were sometime touched by a presynaptic bouton at the site where the previously identified spine had been located. We conclude that new spines are most likely to be functional and that pruned spines can be transformed into shaft synapses and thus maintain their functionality within the neuronal network.

Contribution of the GABA shift to the transition from structural initialization to working stage in biologically realistic networks

Biological cortical neurons form functional networks through a complex set of developmental steps. A key process in early development is the transition of the spontaneous network dynamics from slow synchronous activity to a mature firing profile with complex high-order patterns of spikes and bursts. In the present modeling study we investigate the required properties of the network to initialize this transition by the shift of the chloride reversal potential, which switches the effect of the GABA synapses from depolarizing to hyperpolarizing. The simulated networks are generated by a statistical description of parameters for the neuron model and the network architecture.

Adaptation of Shape of Dendritic Spines by Genetic Algorithm

The role of dendritic spines in information processing of a neuron is still not clear. But it is known that they change their shape and size during learning processes. These effects may be important for storing of information (memory). We analyze the influence of shape variations on the electrical signal propagation in a group of dendritic spines by biologically realistic electrical simulation. In order to show the potential of shape changes a genetic algorithm is used to adapt the geometric parameters of the spine group to specific timing of incoming spikes. We can show that such a group of spines can do information processing like coincidence detection just by adjustment of its geometry.

Three-Dimensional Quasi-binary Image Restoration for Confocal Microscopy and Its Application to Dendritic Trees

For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. The images are unsharp because of the (direction dependent) resolution limit. A simple deconvolution is not sufficient for the needed resolution.